Distributed Resource Allocation via ADMM over Digraphs

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openAccess

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Volume Title

A4 Artikkeli konferenssijulkaisussa

Date

2023-01-10

Department

Department of Electrical Engineering and Automation

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Mcode

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Language

en

Pages

7
5645-5651

Series

2022 IEEE 61st Conference on Decision and Control (CDC)

Abstract

In this paper, we solve the resource allocation problem over a network of agents, with edges as communication links that can be unidirectional. The goal is to minimize the sum of allocation cost functions subject to a coupling constraint in a distributed way by using the finite-time consensus-based alternating direction method of multipliers (ADMM) technique. In contrast to the existing gradient descent (GD) based distributed algorithms, our approach can be applied to non-differentiable cost functions. Also, the proposed algorithm is initialization-free and converges at a rate of $\mathcal{O}\left( {1/k} \right)$, where k is the optimization iteration counter. The fast convergence performance related to iteration counter k compared to state-of-the-art GD based algorithms is shown via a simulation example.

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Keywords

Couplings, Directed graphs, Linear programming, Cost function, Convex functions, Large-scale systems, Resource management, Distributed optimization, ADMM, resource allocation, finite-time consensus, digraphs

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Citation

Jiang, W, Doostmohammadian, M & Charalambous, T 2023, Distributed Resource Allocation via ADMM over Digraphs . in 2022 IEEE 61st Conference on Decision and Control (CDC) . Proceedings of the IEEE Conference on Decision & Control, IEEE, pp. 5645-5651, IEEE Conference on Decision and Control, Cancun, Mexico, 06/12/2022 . https://doi.org/10.1109/CDC51059.2022.9993326